Utiliza este identificador para citar o vincular este elemento: http://hdl.handle.net/10553/42866
Títulos: Experiments and reference models in training neural networks for short-term wind power forecasting in electricity markets
Autores/as: Mendez, Juan 
Lorenzo, Javier 
Hernández, Mario 
Clasificación UNESCO: 120304 Inteligencia artificial
332202 Generación de energía
3322 Tecnología energética
Palabras clave: Energía eólica
Redes neuronales
Fecha de publicación: 2009
Revistas: Lecture Notes in Computer Science 
Resumen: Many published studies in wind power forecasting based on Neural Networks have provided performance factors based on error criteria. Based on the standard protocol for forecasting, the published results must provide improvement criteria over the persistence or references models of its same place. Persistence forecasting is the easier way of prediction in time series, but first order Wiener predictive filter is an enhancement of pure persistence model that have been adopted as the reference model for wind power forecasting. Pure enhanced persistence is simple but hard to beat in short-term prediction. This paper shows some experiments that have been performed by applying the standard protocols with Feed Forward and Recurrent Neural Networks architectures in the background of the requirements for Open Electricity Markets.
URI: http://hdl.handle.net/10553/42866
ISBN: 978-3-642-02477-1
978-3-642-02478-8
ISSN: 0302-9743
DOI: 10.1007/978-3-642-02478-8_161
Aparece en la colección:Actas de Congresos

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